For GNN pre-training, existing studies mostly focus on the design of suitable tasks,
such as graph structure reconstruction [1]}, [2]}, [3]},
mutual information maximization [4]}, [5]}, [6]}
and properties prediction [2]}.
Generally, these tasks can be classified into two main categories:
(1) node-level tasks utilize node representations to predict the localized properties in the graph (e.g., link prediction);
(2) graph-level tasks focus on the entire graph and learn graph representations when designing the globalized optimization goal (e.g., graph's property prediction).
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